Data Preparation
00_DataPreparation
SarahL.
Jun 5 2024 19:00:15
Last updated: 2024-06-05
Checks: 7 0
Knit directory: RA_SingleCellAnalysis/
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Data Preparation and Overview
Setup
Standard packages
library(here)
source(here("code", "standard_libraries.R"))Additional Packages
suppressPackageStartupMessages({
library(DropletUtils)
library(scDblFinder)
})Set parameter
set.seed(123)
bpp <- BiocParallel::MulticoreParam(parallel::detectCores()-1, RNGseed=123)
path <- here::here()Reports
CellRanger
View the CellRanger html report output for each sample
CellBender
View the CellBender html report output for each sample
Cellranger Sample Metrics
Read in the cellranger sample metrics csv files
foldernames <- list.dirs(paste0(path,"/data/cellranger_data_h5") ,recursive = F, full.names = T)
CR.metrics <- lapply(foldernames, function(i){
# remove _Counts is if names don't include them
metrics <- read.csv(file.path(paste0(i),"metrics_summary.csv"), colClasses = "character")
})
experiment.metrics <- do.call("rbind", CR.metrics)
rownames(experiment.metrics) <- list.dirs(paste0(path,"/data/cellranger_data_h5") ,recursive = F, full.names = F)
# Rename some rows/cols
colnames(experiment.metrics) <- gsub("\\."," ", colnames(experiment.metrics))
rownames(experiment.metrics) <-sub("\\_.*","\\1",rownames(experiment.metrics))
# Print table
experiment.metricsLoad Data
Load Cellbender output matrices
Load in the filtered feature barcode matrices from Cellbender of each sample and save them as sce object.
#Load Dataset
rawdata_folder <- paste0(path,"/data/cellbender_data_h5/")
#Get file names
filenames <- list.files(rawdata_folder ,recursive = F, full.names = F,pattern = "\\_filtered.h5$")
filepaths <- paste(rawdata_folder,filenames,sep = "")
#Load data as sce object
sce<-read10xCounts(samples = filepaths, sample.names = filenames,col.names=T,type="HDF5")
#Edit Filenames
colData(sce)$Sample = sub("\\_w_introns.*", "", sub("cellbender_output_", "", as.character(colData(sce)$Sample)))
sceclass: SingleCellExperiment
dim: 36601 119652
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
ENSG00000277196
rowData names(3): ID Symbol Type
colnames(119652): 1_AAGTACCGTCTCTCAC-1 1_GACTCTCAGGTAACTA-1 ...
17_AGGTGTTGTGGCATCC-1 17_GCCAGCACACAAGTGG-1
colData names(2): Sample Barcode
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):Load and add Metadata
#Metadata path
metadata_folder <- paste0(path,"/data/metadata/Metadata_Master.csv")
#Load metadata
metadata_df <-read.csv(metadata_folder, header=TRUE, sep=",")
metadata_df#Add metadata to sce
colData(sce) <- dplyr::left_join(as.data.frame(colData(sce)),
metadata_df,
by= c("Sample" = "Sample"),
suffix=c(".x",".y")) %>%
#dplyr::select(Sample, Barcode, Diagnosis, sex, Age, Joint.Location, protocol, Pathotype,Krenn total score,) %>%
dplyr::select(-one_of("Comments")) %>% #select all except
DataFrame(row.names=colnames(sce))
#Set Sample names
names(colData(sce))[which(names(colData(sce))=="Sample")]="Orig.Identifier"
names(colData(sce))[which(names(colData(sce))=="ID")]="Sample"
#make col names unique
colnames(sce) <- paste0(sce$Sample, ".", sce$Barcode)Annotate and Update genes
#Change Name
names(rowData(sce))[which(names(rowData(sce))=="ID")]="ENSEMBL"
#Annotate genes
AnnoGene <- annotate_genes(data.frame(rowData(sce)), gene_col = "Symbol")
#Add Annotated and Updated genes
rowData(sce) <- AnnoGene
#Make row and col names unique
rownames(sce) <- paste0(rowData(sce)$ENSEMBL, ".", rowData(sce)$Symbol)Dimensions of the count matrix
#Dimensions of count matrix
dim(sce)[1] 36601 119652Explore dataset
#Feautures/row data
data.frame(colData(sce))#Droplet details / row data
data.frame(rowData(sce))Exclude Samples
sce <- sce[,sce$Sample != "SynBio_130"]
dim(sce)[1] 36601 110640Exploratory plots
Histogramm with number of cells
Show the number of cells detected in each sample or joint location before filtering
Per Sample
#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Sample))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample") +
theme_classic()+
stat_count(geom = "text", colour = "white", size = 3.5,aes(label = ..count..),position=position_stack(vjust=0.65))+
theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())+
scale_y_continuous(expand = c(0,0))
data.frame(as.list(table(colData(sce)$Sample)))Per Joint Location
#Histogramm with number of cells per Joint
ggplot(colData(sce), aes(x=Joint.Location))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per Joint") +
theme_classic()+
stat_count(geom = "text", colour = "white", size = 3.5,aes(label = ..count..),position=position_stack(vjust=0.8))+
theme(panel.grid.major.y = element_blank(),panel.grid.minor.y = element_blank())+
scale_y_continuous(expand = c(0,0))
data.frame(as.list(table(colData(sce)$Joint.Location)))Plot number of genes detected per cell
This plot shows cell counts per sample / count occurrence
#Number of genes detected per cell
#Total UMI for a gene versus the number of times detected
genesPerCell <- colSums(counts(sce) > 0)
plot(density(genesPerCell),
xlab="Genes per cell",
main="Number of genes detected per cell")
Plot transcript capture efficiency
This plot gives an idea about the sequencing depth and if the sequencing has reached saturation or not. Plotted is the total gene count across all cells (x-axis) vs Proportion of cells the gene is detected in (y-axis) where each dot represents a gene.
#transcript_capture_efficiency
#Total UMI for a gene versus the number of times detected
tmpCounts <- counts(sce)
plot(rowSums(tmpCounts),
rowMeans(tmpCounts > 0),
log = "x",
xlab="total number of UMIs",
ylab="proportion of cells expressing the gene",
main="Total UMI for a gene vs times detected")
Initial Filtering
Initial filtering to remove unexpressed genes and cells with very low number of counts. The input to scDblFinder should not include empty droplets, and it might be necessary to remove cells with a very low coverage (e.g. <200 reads) to avoid errors. Further quality filtering should be performed downstream of doublet detection.
#Initial filtering before droplet removal
dim_before_filtering <- dim(sce)
#Get only the detected Genes
#Remove Cells with very low counts of less than 150 and genes not expressed
sce <- sce[rowSums(counts(sce)> 0) > 0, colSums(counts(sce)> 0) > 150]
dim_after_filtering <- dim(sce)
#Give Stats
cat("NR of Cells Before Initial Filtering: ", dim_before_filtering[2],"\n",
"NR of Cells After Initial Filtering: ", dim_after_filtering[2],"\n",
"NR of Cells Filtered out: ", dim_before_filtering[2] - dim_after_filtering[2],"\n",
"Cells Filtered out: [%]", (dim_before_filtering[2] - dim_after_filtering[2])/dim_before_filtering[2]*100,"\n",
"\n",
"NR of Genes Before Initial Filtering: ", dim_before_filtering[1],"\n",
"NR of Genes After Initial Filtering: ", dim_after_filtering[1],"\n",
"NR of Genes Filtered out: ", dim_before_filtering[1] - dim_after_filtering[1],"\n",
"Genes Filtered out: [%]", (dim_before_filtering[1] - dim_after_filtering[1])/dim_before_filtering[1]*100,"\n"
)NR of Cells Before Initial Filtering: 110640
NR of Cells After Initial Filtering: 84318
NR of Cells Filtered out: 26322
Cells Filtered out: [%] 23.79067
NR of Genes Before Initial Filtering: 36601
NR of Genes After Initial Filtering: 34550
NR of Genes Filtered out: 2051
Genes Filtered out: [%] 5.603672 Doublet Detection
Doublets are defined as two cells that are sequenced under the same cellular barcode, which happens if they were captured in the same droplet. The scDblFinder method combines the strengths of various doublet detection approaches, training an iterative classifier on the neighborhood of real cells and artificial doublets. Doublet removal is performed on feature-barcode matrix after a initial filtering to remove Cells with very low counts of less than 200 counts and genes not expressed to ensure no potential empty droplets.
Detection
#Detection
sce <- scDblFinder::scDblFinder(sce, samples=sce$Sample, BPPARAM = bpp)
table(sce$scDblFinder.class)
singlet doublet
76772 7546 as.data.frame.matrix(table(sce$Sample,sce$scDblFinder.class))Doublet Detection Plots
Singlet/Doublet Histogramm
Absolute comparison
#Plot singlet/doublet histogramm Absolute comparison / by sample
as.data.frame(colData(sce)) %>%
dplyr::group_by(Sample, scDblFinder.class) %>%
dplyr::summarise(Freq=n()) %>%
ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class,label=Freq)) +
geom_bar(stat="identity") +
labs(title="Doublet detection results",
subtitle="By Sample",x="",y="Number of Cells") +
geom_text(size=3, position = position_stack(vjust=0.5))+
theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))+
scale_y_continuous(expand = c(0,0)) +
scale_fill_manual(values = c("#4dc8c9","#FB8072"))
Relative comparison
# Plot singlet/doublet histogramm Relative comparison / by sample
as.data.frame(colData(sce)) %>%
dplyr::group_by(Sample, scDblFinder.class) %>%
dplyr::summarise(Freq=n()) %>%
ggplot(aes(x=Sample, y=Freq, fill=scDblFinder.class, label=Freq)) +
geom_bar(stat="identity", position="fill") +
labs(title="Doublet detection results",
subtitle="By Sample",
x="Sample",
y="Number of cells") +
geom_text(size=2.5, position = position_fill(vjust=0.5)) +
theme(axis.text.x = element_text(angle = 45,hjust=1), axis.line = element_line(colour = "black"), panel.background = element_rect(fill = NA))+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(values = c("#4dc8c9","#FB8072"))
Singlet/Doublet Scatter
sce <- addPerCellQC(sce)
#Plot singlet/doublet qcplots
colData(sce) %>%
as.data.frame() %>%
arrange(scDblFinder.class) %>%
ggplot(aes(x = sum, y = detected )) +
geom_point(aes(colour = scDblFinder.class),size=0.5) +
facet_wrap(vars(Sample))+
labs(title="Total number of detected genes plotted against total number of UMIs",
x="Total counts",
y="Detected genes") +
theme(strip.background=element_rect(fill="white"), panel.background = element_rect(fill = NA),axis.line = element_line(colour = "black"))+
scale_fill_manual(values = c("#FB8072","#4dc8c9"))
Apply Doublet Removal
#Doublet Removal
dim_before_doublet <- dim(sce)
#Apply Doublet Removal
sce <- sce[ ,sce$scDblFinder.class == "singlet"]
dim_after_doublet <- dim(sce)
#Give Stats
cat("NR of Cells Before Doublet Removal: ", dim_before_doublet[2],"\n",
"NR of Cells After Doublet Removal: ", dim_after_doublet[2],"\n",
"NR of Cells Filtered out: ", dim_before_doublet[2] - dim_after_doublet[2],"\n",
"Cells Filtered out: [%]", (dim_before_doublet[2] - dim_after_doublet[2])/dim_before_doublet[2]*100,"\n"
)NR of Cells Before Doublet Removal: 84318
NR of Cells After Doublet Removal: 76772
NR of Cells Filtered out: 7546
Cells Filtered out: [%] 8.949453 Save the dataset
saveRDS(sce, file =paste0(path,'/output/00_sce_DataPreparation.rds'))
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Warsaw
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] scDblFinder_1.16.0 DropletUtils_1.22.0
[3] tidyr_1.3.1 org.Hs.eg.db_3.18.0
[5] AnnotationDbi_1.64.1 clusterProfiler_4.10.1
[7] viridis_0.6.5 viridisLite_0.4.2
[9] gridExtra_2.3 scran_1.30.2
[11] scater_1.30.1 scuttle_1.12.0
[13] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[15] Biobase_2.62.0 GenomicRanges_1.54.1
[17] GenomeInfoDb_1.38.8 IRanges_2.36.0
[19] S4Vectors_0.40.2 BiocGenerics_0.48.1
[21] MatrixGenerics_1.14.0 matrixStats_1.3.0
[23] dplyr_1.1.4 ggplot2_3.5.1
[25] BiocParallel_1.36.0 here_1.0.1
[27] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.3.3 later_1.3.2
[3] BiocIO_1.12.0 bitops_1.0-7
[5] ggplotify_0.1.2 R.oo_1.26.0
[7] tibble_3.2.1 polyclip_1.10-6
[9] XML_3.99-0.16.1 lifecycle_1.0.4
[11] edgeR_4.0.16 rprojroot_2.0.4
[13] processx_3.8.4 lattice_0.22-5
[15] MASS_7.3-60.0.1 magrittr_2.0.3
[17] limma_3.58.1 sass_0.4.9
[19] rmarkdown_2.27 jquerylib_0.1.4
[21] yaml_2.3.8 metapod_1.10.1
[23] httpuv_1.6.15 cowplot_1.1.3
[25] DBI_1.2.2 RColorBrewer_1.1-3
[27] abind_1.4-5 zlibbioc_1.48.2
[29] R.utils_2.12.3 purrr_1.0.2
[31] ggraph_2.2.1 RCurl_1.98-1.14
[33] yulab.utils_0.1.4 tweenr_2.0.3
[35] git2r_0.33.0 GenomeInfoDbData_1.2.11
[37] enrichplot_1.22.0 ggrepel_0.9.5
[39] irlba_2.3.5.1 tidytree_0.4.6
[41] dqrng_0.4.0 DelayedMatrixStats_1.24.0
[43] codetools_0.2-19 DelayedArray_0.28.0
[45] DOSE_3.28.2 ggforce_0.4.2
[47] tidyselect_1.2.1 aplot_0.2.2
[49] farver_2.1.2 ScaledMatrix_1.10.0
[51] GenomicAlignments_1.38.2 jsonlite_1.8.8
[53] BiocNeighbors_1.20.2 tidygraph_1.3.1
[55] tools_4.3.3 treeio_1.26.0
[57] Rcpp_1.0.12 glue_1.7.0
[59] SparseArray_1.2.4 xfun_0.44
[61] qvalue_2.34.0 HDF5Array_1.30.1
[63] withr_3.0.0 BiocManager_1.30.23
[65] fastmap_1.2.0 rhdf5filters_1.14.1
[67] bluster_1.12.0 fansi_1.0.6
[69] callr_3.7.6 digest_0.6.35
[71] rsvd_1.0.5 R6_2.5.1
[73] gridGraphics_0.5-1 colorspace_2.1-0
[75] GO.db_3.18.0 RSQLite_2.3.6
[77] R.methodsS3_1.8.2 utf8_1.2.4
[79] generics_0.1.3 renv_1.0.7
[81] data.table_1.15.4 rtracklayer_1.62.0
[83] graphlayouts_1.1.1 httr_1.4.7
[85] S4Arrays_1.2.1 scatterpie_0.2.2
[87] whisker_0.4.1 pkgconfig_2.0.3
[89] gtable_0.3.5 blob_1.2.4
[91] XVector_0.42.0 shadowtext_0.1.3
[93] htmltools_0.5.8.1 fgsea_1.28.0
[95] scales_1.3.0 png_0.1-8
[97] ggfun_0.1.4 knitr_1.45
[99] rstudioapi_0.16.0 rjson_0.2.21
[101] reshape2_1.4.4 nlme_3.1-164
[103] rhdf5_2.46.1 cachem_1.1.0
[105] stringr_1.5.1 parallel_4.3.3
[107] vipor_0.4.7 HDO.db_0.99.1
[109] restfulr_0.0.15 pillar_1.9.0
[111] grid_4.3.3 vctrs_0.6.5
[113] promises_1.3.0 BiocSingular_1.18.0
[115] beachmat_2.18.1 cluster_2.1.6
[117] beeswarm_0.4.0 evaluate_0.23
[119] Rsamtools_2.18.0 cli_3.6.2
[121] locfit_1.5-9.9 compiler_4.3.3
[123] rlang_1.1.3 crayon_1.5.2
[125] labeling_0.4.3 ps_1.7.6
[127] getPass_0.2-4 plyr_1.8.9
[129] fs_1.6.4 ggbeeswarm_0.7.2
[131] stringi_1.8.4 munsell_0.5.1
[133] Biostrings_2.70.3 lazyeval_0.2.2
[135] GOSemSim_2.28.1 Matrix_1.6-5
[137] patchwork_1.2.0 sparseMatrixStats_1.14.0
[139] bit64_4.0.5 Rhdf5lib_1.24.2
[141] KEGGREST_1.42.0 statmod_1.5.0
[143] highr_0.10 igraph_2.0.3
[145] memoise_2.0.1 bslib_0.7.0
[147] ggtree_3.10.1 fastmatch_1.1-4
[149] xgboost_1.7.7.1 bit_4.0.5
[151] gson_0.1.0 ape_5.8